星载微波资料频率干扰信号识别研究综述

较可见光和红外遥感而言,微波遥感不易受大气影响,具有全天时、全天候的监测能力以及对云、雨、大气较强的穿透能力,并且微波传感器对于植被特性的变化、地表土壤水分和积雪参数十分敏感,微波数据已被广泛应用于地表参数的监测和反演应用之中.然而,用于反演地表参数的低频微波观测资料均不同程度地受到地面无线电频率的干扰(Radio Frequency Interference,RFI).这些干扰往往是由地面主动微波传感器的发射信号或陆面反射辐射信号产生的,很容易覆盖地表产生的相对较弱的自然热发射辐射信号,使得星载被动微波传感器接收的信息不能真实地反映地表状况.如果不能准确地将其识别和剔除,往往导致较大的反演误差,降低遥感数据反演产品的质量,从而显著降低现有以及将来的被动微波资料的利用率.本文从目前常用的干扰识别方法,包括谱差法、平均值和标准差法、多通道回归法、主分量分析法和一维变分反演收敛度量识别法等等,回顾了识别星载微波辐射计数据中RFI信号的研究进程及其研究中存在的问题,并对这些方法的优、缺点分别进行了评价,阐述了存在的问题.最后对星载微波资料RFI识别的研究做出展望,指出今后应进一步完善RFI信号的识别方法,开发RFI信号的订正算法,将其应用到卫星遥感数据的产品反演与同化过程中,并获取可靠的陆面、洋面RFI源分布和分类信息,更好地评估多种识别方法的可靠性、准确性和适用性.     

卫星遥感微波地表发射率研究综述

在大气参数反演、卫星资料同化及微波降雨算法等研究领域均需要全球尺度上较为精确的微波地表发射率数据的支持,而微波地表发射率目前难以精确的获取.鉴于此,本文介绍了三种主要的微波地表发射率估算方法,并从卫星遥感的角度阐述当前微波地表发射率的估算、应用现状及存在的问题,最后对微波地表发射率的研究做出展望.     

几种地表微波比辐射率变化特征的地面观测

基于地表微波比辐射率观测试验,我们探讨了不同下垫面的地表微波比辐射率的变化特征以及降雨前后地表比辐射率的昼夜变化特征.同时,通过比较红外扫描仪和温度计同步测量的地表温度,发现将温度计浅埋土里比半埋土里测量的更为合理,后者测量的地表温度在中午时不合理偏高15～20℃.对于四种地表,草地比辐射率最高(～0.94),其次是裸土地比辐射率(～0.86),然后是沙地比辐射率(～0.82),水面比辐射率最小(～0.4).在微波辐射计观测入射角 > 60°时,土地和沙地比辐射率都随入射角度增加而减小,尤其前者更为敏感;草地和水面比辐射率随入射角度变化较小.不同地表比辐射率都呈现出昼夜差异,尤其土地、沙地和水面比辐射率在降雨之后的昼夜差异较为显著,夜里普遍偏高白天0.02～0.04;草地比辐射率昼夜差异较小,基本是白天略微高于晚上.降水后,草地微波比辐射率变化较小,裸土地和沙地比辐射率则显著降低.沙地和草地比辐射率随频率变化较小,裸土地比辐射率在降雨之后随频率明显增加.     

基于卫星遥感资料的中国区域土壤湿度EnKF数据同化

土壤湿度在陆气相互作用过程中扮演着重要的角色,是气候、水文、农业、林业等研究中重要的地球物理参数之一.土壤湿度影响地面蒸散,径流、地表反射率、地表发射率以及地表感热和潜热通量,从而对气候有重要影响,它对大气的影响在全球尺度上仅次于海面温度,在陆地尺度其影响甚至超过海面温度.本文介绍了基于EnKF及陆面过程模型的中国区域陆面土壤湿度同化系统（CLSMDAS,China Land Soil Moisture Data Assimilation System）,以及该系统应用于中国区域陆面土壤湿度同化试验的结果.CLSMDAS包括以下几个部分：1）陆面模式采用美国国家大气研究中心NCAR的陆面过程模型Community Land Model Version3.0（简写为CLM3.0）;2）大气驱动场数据中的降水和地面入射太阳辐射数据来自FY2静止气象卫星每小时产品;3）陆面数据同化方法采用EnKF（Ensemble Kalman Filter）同化方法;4）观测数据包括AMSR-E卫星反演土壤湿度产品以及地面土壤湿度观测资料.利用CLSMDAS对2006年6~9月的土壤湿度同化试验结果的分析表明：陆面模式模拟和同化结果都能比较合理地反映出土壤湿度时空分布,同化的土壤湿度分布与2006年8月重庆、四川发生建国以来最严重的夏伏旱有非常好的对应关系,与发生在9月的湖北东部、广西南部等地的干旱区也有非常好的对应关系.     

沙漠陆面过程参数化与模拟

沙漠地区植被稀疏、干旱少雨,其陆面物理过程具有与全球其它地区显著不同的特点.本文利用巴丹吉林沙漠观测资料,分析和计算了地表反照率、比辐射率、粗糙度和土壤热容量、热传导系数等关键陆面过程参数,建立了适合于沙漠地区的陆面过程模式DLSM (Desert Land Surface Model),并与NOAH陆面过程模式的模拟结果和观测资料进行了比较.结果表明:巴丹吉林沙漠地表反照率为0.273,比辐射率为0.950,地表粗糙度为1.55×10^-3 m,土壤热容量和热扩散系数分别为1.08×10⁶ J·m^-3·K^-1和3.34×10^-7 m²·s.辐射传输、感热输送和土壤热传导过程是影响沙漠地区地表能量平衡的主要物理过程.通过对这三种过程的准确模拟检验,DLSM能够较准确地模拟巴丹吉林沙漠地气能量交换特征;短波辐射、长波辐射和感热通量的模拟结果与观测值间的标准差分别为7.98,6.14,33.9 W·m^-2,与NOAH陆面过程模式的7.98,7.72,46.6 W·m^-2的结果接近.地表反照率是沙漠地区最重要的陆面过程参数,地表反照率增大5%,向上短波辐射通量随之增加5%,感热通量则减小2.8%.本文研究结果对丰富陆面过程参数化方案,改进全球陆面过程模式、气候模式具有参考意义.     

GMS 5资料反演地表温度的一个修正算法

本文研究发展利用GMS 5/VISSR每小时卫星观测资料反演地表温度的方法,首先利用时空判断法进行云检测寻找晴空像元,然后从辐射传输方程出发,由实时探空资料求取大气上行、下行辐射率及大气透过率,根据由AVHRR NDVI导出的地表比辐射率,用单时相双光谱分裂窗法反演得到地表温度.比较反演结果与54511站及其他中国基准站2000年地面0cm地表温度实测值,相对于国际上其他经验公式而言,本文算法在精度上有所提高.敏感性分析试验着重于大气衰减的影响.基于本文算法,给出了内蒙中东部地区地表温度连续4天的变化实例以及东亚部分陆地“纯晴天”地表温度图.     

基于简化参数方法的蒙古干旱区土壤湿度被动微波遥感

卫星被动微波遥感土壤湿度,是准确分析大空间尺度上陆表水分变化信息的有效手段.美国航天局(NASA)发布的基于AMSR-E观测亮温资料的全球土壤湿度反演产品,在蒙古干旱区的实际精度并不令人满意.本文基于对地表微波辐射传输中地表粗糙度和植被层影响的简化处理方法,采用AMSR-E的6.9 GHz,10.7 GHz和18.7 GHz之V极化亮温资料,应用多频率反演算法,并以国际能量和水循环协同观测计划(The Coordinated Energy and Water Cycle Observations Project)即CEOP实验在蒙古国东部荒漠地区的地面实验资料作为先验知识,获取被动微波遥感模型的优化参数,以期获得蒙古干旱区精度更高的土壤湿度遥感估算结果.分析表明,本文方法反演的白天和夜间土壤湿度结果与地面验证值之间的均方根误差(RMSE)接近0.030 cm³/cm³, 证明所用方法在不需要其他辅助资料或参数帮助下,可较精确地反演干旱区表层土壤湿度信息,能够全天候、动态监测大空间尺度的土壤湿度变化,可为干旱区气候变化研究及陆面过程模拟和数据同化研究提供高精度的表层土壤湿度初始场资料.     

基于残差重采样粒子滤波的土壤水分估算和水力参数同步优化

陆面数据同化由于能将观测数据和模型模拟有机结合,已逐步发展为地球科学研究的重要方法之一.通过数据同化方法在模型中不断融入新的观测数据,一方面可以有效地校正陆面过程模型的预测轨迹,提高模型状态变量的估算精度,另一方面可以不断减小模型中的不确定因素,优化模型中的相关参数.在众多数据同化算法中,粒子滤波算法不受模型线性和误差高斯分布假设的约束,适用于任意非线性非高斯动态系统,逐渐成为当前数据同化算法研究的热点.本研究基于残差重采样粒子滤波算法发展了一个数据同化方案,将微波亮温数据同化到大尺度半分布式VIC(Variable Infiltration Capacity)陆面水文模型中,对土壤水分进行估算,并对模型中的三个水力参数进行同步优化.最后设计了一系列对比实验并利用美国亚利桑那州在SMEX04(Soil Moisture Experiment 2004)期间获取的一套完整的实验数据对该同化方案进行了验证.结果表明,该同化方案能够大幅度提高土壤水分估算精度,同时模型中的三个水力参数也得到了较好的优化,从而证明了该数据同化方案的有效性.     

中尺度涡的高度计资料同化模拟

中尺度涡在大洋环流中有着重要的作用. 为检验高度计资料同化应用于中尺度涡模拟的效果, 提高环流模式对中尺度涡模拟的精度, 使用三维变分的OVALS同化系统结合POM模式进行了中尺度涡同化模拟实验. 该系统将卫星高度计资料同化反演成为温盐伪观测数据, 然后再次进行常规温盐同化, 得到温盐分析场. 根据中尺度涡现象的物理特性, 经过对比检验, 对系统中的同化参数作了合理设置. 使用T/P和ERS1/2的10 a卫星高度计的海表高度异常资料, 分别用同化与非同化两种方案对西北太平洋的中尺度涡进行对比模拟实验. 将实验结果与观测数据的比较表明, 加入高度计资料同化的模拟结果远远好于未使用同化的模拟结果. 说明高度计反演温盐场的同化方法用于对于模拟中尺度涡现象是非常有效的.     

半干旱区关键地表参数的估算及其对地气通量模拟的改进

陆面过程模式中有关土壤水热传输、植被冠层和空气动力学等过程的关键参数的不确定性严重制约着地表-大气相互作用模拟能力的提高.本文利用架设在我国吉林通榆和甘肃榆中典型半干旱区陆-气相互作用野外试验站的观测资料,结合大气边界层理论,利用多种方法系统估算了上述试验站地表空气动力学粗糙度(z0m)以及热传输附加阻尼(?B?1)的量值.结果表明,z0m在半干旱区具有明显的季节变化和年际变化特征,且在植被低矮的下垫面与现行通用的陆面模式中的默认值相差较大;而?B?1的日变化和季节变化特征明显.将修正后的z0m和?B?1参数化方案引入陆面过程模式,发现能够明显改善模式对于半干旱区地表感热通量的模拟能力.这些结果说明有必要进一步结合半干旱区的野外观测试验对陆面模式在该地区的缺省参数设置进行更广泛的评估,而基于外场观测试验和大气边界层理论估算的关键地表参数对于改进陆气相互作用的模拟体现出较大的应用潜力.     

A review of the estimation of downward surface shortwave radiation based on satellite data: Methods,progress and problems

The estimation of downward surface shortwave radiation(DSSR) is important for the Earth's energy budget and climate change studies. This review was organised from the perspectives of satellite sensors, algorithms and future trends,retrospects and summaries of the satellite-based retrieval methods of DSSR that have been developed over the past 10 years. The shortwave radiation reaching the Earth's surface is affected by both atmospheric and land surface parameters. In recent years,studies have given detailed considerations to the factors which affect DSSR. It is important to improve the retrieval accuracy of cloud microphysical parameters and aerosols and to reduce the uncertainties caused by complex topographies and high-albedo surfaces(such as snow-covered areas) on DSSR estimation. This review classified DSSR retrieval methods into four categories:empirical, parameterisation, look-up table and machine-learning methods, and evaluated their advantages, disadvantages and accuracy. Further efforts are needed to improve the calculation accuracy of atmospheric parameters such as cloud, haze, water vapor and other land surface parameters such as albedo of complex terrain and bright surface, organically combine machine learning and other methods, use the new-generation geostationary satellite and polar orbit satellite data to produce highresolution DSSR products, and promote the application of radiation products in hydrological and climate models.     

Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data

Water vapor plays a crucial role in atmospheric processes that act over a wide range of temporal and spatial scales, from global climate to micrometeorology. The determination of water vapor distribution in the atmosphere and its changing pattern is very important. Although atmospheric scientists have developed a variety of means to measure precipitable water vapor(PWV) using remote sensing data that have been widely used, there are some limitations in using one kind satellite measurements for PWV retrieval over land. In this paper, a new algorithm is proposed for retrieving PWV over land by combining different kinds of remote sensing data and it would work well under the cloud weather conditions. The PWV retrieval algorithm based on near infrared data is more suitable to clear sky conditions with high precision. The 23.5 GHz microwave remote sensing data is sensitive to water vapor and powerful in cloud-covered areas because of its longer wavelengths that permit viewing into and through the atmosphere. Therefore, the PWV retrieval results from near infrared data and the indices combined by microwave bands remote sensing data which are sensitive to water vapor will be regressed to generate the equation for PWV retrieval under cloud covered areas. The algorithm developed in this paper has the potential to detect PWV under all weather conditions and makes an excellent complement to PWV retrieved by near infrared data. Different types of surface exert different depolarization effects on surface emissions, which would increase the complexity of the algorithm. In this paper, MODIS surface classification data was used to consider this influence. Compared with the GPS results, the root mean square error of our algorithm is 8 mm for cloud covered area. Regional consistency was found between the results from MODIS and our algorithm. Our algorithm can yield reasonable results on the surfaces covered by cloud where MODIS cannot be used to retrieve PWV.     

Synchronous retrieval of land surface temperature and emissivity

This is an old topic for more than ten years to retrieve land surface temperature (LST) from satellite data, but it has not been solved yet. At first, people tried to transplant traditional split window method of sea surface temperature (SST) to the retrieval of LST, but it was found that the emissivities of land surface (ε_i) must be involved in atmospheric correction. Then many different formulas appeared with assumption of emissivities known. In fact, emissivities of land surface with pixel size cannot be known beforehand because of various reasons, so in recent years the focus of attention has been transferred to retrieving emissivities (ε_i) and LST at the same time. Therefore, we have to solve missing equations problem. For this some people try to introduce middle infrared information, but new problems will be brought in which means that it is very difficult to describe middle infrared BRDF of targets with high accuracy and the scattering of atmospheric aerosol cannot be ignored. Therefore a different way is offered to solve this problem only using two thermo-infrared bands data based on three assumptions, constant emissivities in two measurements, and the same atmospheric parameters for neighbouring pixels and the difference of emissivity (Δε) of two channels can be known beforehand. Results of digital simulations show that it is possible to retrieve LST with its root mean square (RMS) of errors less than 1 K and RMS of relative error of ground radiance at 7% if the error of atmospheric temperature at ± 2°C and the relative error of atmospheric water vapor at ± 10% can be satisfied. Results have been confirmed by initial field test. Project supported by the National Natural Science Foundation of China (Grant No. 49471056) and China’s National Key Basic Research Plan.     

Evaluating the potential of a blended passive microwave‐interactive multi‐sensor product for improved mapping of snow cover and estimations of snow water equivalent

The retrieval of Snow Water Equivalent (SWE) from remote sensing satellites continues to be a very challenging problem. In this paper, we evaluate the accuracy of a new SWE product derived from the blending of a passive microwave SWE product based on the Advanced Microwave Sounding Unit (AMSU) with a multi‐sensor snow cover extent product based on the Interactive Multi‐sensor Snow and Ice Mapping System (IMS). The microwave measurements have the ability to penetrate the snow pack, and thus, the retrieval of SWE is best accomplished using the AMSU. On the other hand, the IMS maps snow cover more reliably due to the use of multiple satellite and ground observations. The evolution of global snow cover from the blended, the AMSU and the IMS products was examined during the 2006 snow season. Despite the overall good inter‐product agreement, it was shown that the retrievals of snow cover extent in the blended product are improved when using IMS, with implications for improved microwave retrievals of SWE. In a separate investigation, the skill of the microwave SWE product was also examined for its ability to correctly estimate SWE globally and regionally. Qualitative evaluation of global SWE retrievals suggested dependence on land surface temperature: the lower the temperature, the higher the SWE retrieved. This temperature bias was attributed in part to temperature effects on those snow properties that impact microwave response. Therefore, algorithm modifications are needed with more dynamical adjustments to account for changing snow cover. Quantitative evaluation over Slovakia in central Europe, for a limited period in 2006, showed reasonably good performance for SWE less than 100 mm. Sensitivity to deeper snow decreased significantly. Copyright © 2007 John Wiley & Sons, Ltd.     

Aerosol retrieval over land by exploiting the synergy of TERRA and AQUA MODIS DATA

Aerosol particles over land mainly come from man- made source such as biomass burning, industrial de-bris and natural source such as soil dust, sea salt parti-cles, etc. More and more research results show that, aerosols impact global and regional energy radiative budget; aerosol particles also modify the cloud mi-crophysics, as a result, aerosol particles may change the cloud radiative properties. Aerosol particles also play an important role in many biogeochemical cycles. All the above-menti…     

Improvement of snow depth retrieval for FY3B-MWRI in China

The primary objective of this work is to develop an operational snow depth retrieval algorithm for the FengYun3B Microwave Radiation Imager(FY3B-MWRI)in China.Based on 7-year(2002–2009)observations of brightness temperature by the Advanced Microwave Scanning Radiometer-EOS(AMSR-E)and snow depth from Chinese meteorological stations,we develop a semi-empirical snow depth retrieval algorithm.When its land cover fraction is larger than 85%,we regard a pixel as pure at the satellite passive microwave remote-sensing scale.A 1-km resolution land use/land cover(LULC)map from the Data Center for Resources and Environmental Sciences,Chinese Academy of Sciences,is used to determine fractions of four main land cover types(grass,farmland,bare soil,and forest).Land cover sensitivity snow depth retrieval algorithms are initially developed using AMSR-E brightness temperature data.Each grid-cell snow depth was estimated as the sum of snow depths from each land cover algorithm weighted by percentages of land cover types within each grid cell.Through evaluation of this algorithm using station measurements from 2006,the root mean square error(RMSE)of snow depth retrieval is about 5.6 cm.In forest regions,snow depth is underestimated relative to ground observation,because stem volume and canopy closure are ignored in current algorithms.In addition,comparison between snow cover derived from AMSR-E and FY3B-MWRI with Moderate-resolution Imaging Spectroradiometer(MODIS)snow cover products(MYD10C1)in January 2010 showed that algorithm accuracy in snow cover monitoring can reach 84%.Finally,we compared snow water equivalence(SWE)derived using FY3B-MWRI with AMSR-E SWE products in the Northern Hemisphere.The results show that AMSR-E overestimated SWE in China,which agrees with other validations.     

The effect of roughness in simultaneously retrieval of land surface parameters

Using remotely-sensed data, various soil moisture estimation models have been developed for bare soil areas. Previous studies have shown that the brightness temperature (BT) measured by passive microwave sensors were affected by characteristics of the land surface parameters including soil moisture, vegetation cover and soil roughness. Therefore knowledge of vegetation cover and soil roughness is important for obtaining frequent and global estimations of land surface parameters especially soil moisture.In this study, a model called Simultaneous Land Parameters Retrieval Model (SLPRM) that is an iterative least-squares minimization method is proposed. The algorithm estimates surface soil moisture, land surface temperature and canopy temperature simultaneously in vegetated areas using AMSR-E (Advance Microwave Scanning Radiometer-EOS) brightness temperature data. The simultaneous estimations of the three parameters are based on a multi-parameter inversion algorithm which includes model construction, calibration and validation using observations carried out for the SMEX03 (Soil Moisture Experiment, 2003) region in the South and North of Oklahoma.Roughness parameter has also been included in the algorithm to increase the soil parameters retrieval accuracy. Unlike other methods, the SLPRM method works efficiently in all land covers types.The study focuses on soil parameters estimation by comparing three different scenarios with the inclusion of roughness data and selects the most appropriate one. The difference between the resulted accuracies of scenarios is due to the roughness calculation approach.The analysis on the retrieval model shows a meaningful and acceptable accuracy on soil moisture estimation according to the three scenarios.The SLPRM method has shown better performance when the SAR (Synthetic Aperture RADAR) data are used for roughness calculation.     

A new soil freeze/thaw discriminant algorithm using AMSR‐E passive microwave imagery

The soil freeze–thaw controls the hydrological and carbon cycling and thus affects water and energy exchanges at land surface. This article reported a newly developed algorithm for distinguishing the freeze/thaw status of surface soil. The algorithm was based on information from Advanced Microwave Scanning Radiometer Enhanced (AMSR‐E) which records brightness temperature (Tb) in the afternoon and after midnight. The criteria and discriminant functions were obtained from both radiometer observations and model simulations. First of all, the microwave radiation from freeze–thaw soil was examined by carrying out experimental measurements at 18·7 and 36·5 GHz using a Truck‐mounted Multi‐frequency Microwave Radiometer (TMMR) in the Heihe River of China. The experimental results showed that the soil moisture is a key component that differentiates the microwave radiation behaviours during the freeze–thaw process, and the differences in soil temperature and emissivity between frozen and thawed soils were found to be the most important criteria. Secondly, a combined model was developed to consider the impacts of complex ground surface conditions on the discrimination. The model simulations quite followed the trend of in situ observations with an overall relation coefficient (R) of approximately 0·88. Finally, the ratio of Tb18·7H (horizontally polarized Tb at 18·7 GHz) to Tb36·5V was considered primarily as the quasi‐emissivity, which is more reasonable and explicit in measuring the microwave radiation changes in soil freezing and thawing than the spectral gradient. By combining Tb36·5V to indicate the soil temperature variety, a Fisher linear discrimination analysis was used to establish the discriminant functions. After being corrected by TMMR measurements, the new discriminant algorithm had an overall accuracy of 86% when validated by 4‐cm soil temperature. The multi‐year discriminant results also provided a good agreement with the classification map of frozen ground in China. Copyright © 2011 John Wiley & Sons, Ltd.     

应用静止卫星热红外遥感亮温资料反演地表温度的方法研究

双通道多时相反演方法在提取地表温度场时可把地表温度与地表发射率分离计算,是利用静止卫星热红外遥感资料研究地表温度场变化的一种有效方法。该方法计算简单,易于实现海量资料处理,基本满足红外遥感资料在地震预报中的应用要求。     

Review of the applications of Multiangle Imaging SpectroRadiometer to air quality research

The Multiangle Imaging SpectroRadiometer (MISR) launched by NASA in late 1999 has a unique multiangle design, which points nine cameras at fixed angles along the satellite flight track and collects reflected solar radiation simultaneously. This design allows the retrieval of a rich dataset of particle abundance, shape and composition over both land and ocean. Some of its capabilities have not been seen by any currently operating satellite aerosol sensors. Since MISR is sensitive to fine particles, it provides a new data source to study the spatial and temporal characteristics of air quality over large geographical regions. We first briefly introduce the MISR instrument, the retrieval and structure of MISR aerosol data, and then review the applications of MISR aerosol data in various aspects of air quality research since its launch. These include the spatial distributions of particle pollution events such as dust storms, wild fires, and urban pollution. Because of the high quality of MISR aerosol data, they can be used as quantitative indicators of particle pollution levels. We review the current modeling studies of surface level particle concentrations. Next, we introduce research results using MISR’s advanced data such as the plume heights, and particle microphysical properties. In the discussion, we compare MISR research with current MODIS research to the best of our ability as MODIS data have been more extensively explored by the Chinese scientific community. Finally, we summarize the advantages and disadvantages of MISR data related to its applications to the air quality research. Given the highly quantitative measurements and comprehensive aerosol information MISR can provide, we believe that it will provide great values to advance our understanding of the particle air pollution in China. Supported by Harvard-EPA Center on Particle Health Effects (Grant Nos. R-827353 and R-832416), NASA’s Climate and Radiation Research and Analysis Program, the EOS-MISR Instrument Project and the National High Technology Research and Development Program of China (Grant No. 2006AA06A305).